Robust Human-AI Complementarity under Uncertainty

An inverse mixed-integer optimization framework for learning interpretable models of expert decision making

Understanding how experts make decisions and being able to transfer that knowledge is important, especially in complex engineering applications. It is highly valuable for training novices, improving the performance of human-machine systems, and potentially enabling fully autonomous systems that perform as well as human experts. However, an expert's decision-making strategy, developed through years of experience, is often not directly accessible, since the implicit preferences and decision rules involved can be difficult to specify explicitly. This has motivated the use of observed decisions made by the expert to learn an interpretable model that captures the expert's decision-making process. In this work, we develop an inverse optimization approach to jointly learn the decision-maker's preferences (or perceived costs) and the decision rules governing their choices. We demonstrate the general applicability of our approach using three case studies that consider a shift assignment problem, a production planning problem, and a real-world routing problem, respectively. Across these case studies, modeling both perceived costs and decision rules leads to better predictions, highlighting the value of the proposed framework and its greater flexibility in capturing and replicating expert decision making.

The Value of Human Expertise

We consider optimization applications with unknown parameters where the decision maker believes that the optimal value of the nominal problem-the optimization problem they would have solved if the true parameters were known-is unlikely to be large. This belief derives from information that humans have that is not captured in datasets, obtained from domain knowledge and interacting with the physical world. We propose an approach to evaluating policies that provides tighter performance guarantees if the decision maker's belief happens to be correct. Our main result shows that if computing a policy's worst-case performance is a convex program, then the value of human expertise-the maximum improvement in performance guarantees that can be obtained from the belief about the nominal problem-is equal to the minimax gap of a max-min problem. We illustrate our developments in assortment optimization and shortest path problems.

Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach

This work develops a six step BBN framework and illustrates it to model customer intention to consult a doctor in an alternative healthcare system and reveals that while self efficacy appears to be a major factor, its actual causal impact is small.

How generative AI behaves in the newsvendor problem: a behavioral experimental study

This study investigates behavioral biases of generative artificial intelligence (AI) models, specifically GPT-4o and Claude-Haiku-4.5, in inventory management using the newsvendor problem. This study compares AI decision-making with human-subject experiments to assess whether large language models (LLMs) replicate human cognitive bias and to identify prompt-design strategies that improve alignment with optimal outcomes. Controlled newsvendor experiments were conducted with generative AI models, mirroring established human-subject laboratory protocols. Prompt framing was systematically varied across three modifications: removing explicit waste and missed-profit information, simplifying instruction format and providing explicit optimization formulas. Results were benchmarked against normative economic predictions and existing human behavioral findings. Generative AI exhibits human-like human biases including risk aversion, loss aversion and demand chasing, but exhibits a stronger demand-chasing tendency than human participants. It responds to hypothetical incentives and displays bounded rationality. Prompt design significantly influences decision quality, producing decisions closer to theoretical benchmarks. This study empirically tests generative AI behavioral biases within a structured operations management experiment. It introduces a replicable methodology, extends findings across two architecturally distinct LLMs from different developers, and demonstrates that deliberate prompt design meaningfully reduces AI decision bias. The study also contributes a conceptual distinction between functionally analogous behavioral patterns and intrinsic psychological dispositions in LLMs, offering a more precise interpretive framework for AI decision-making research in operational contexts.

Generalizing Bayesian Human-AI Collaboration: Theory and Application in Data-Scarce Environments

The means of prediction and the production function of AI

Who gets to decide what AI systems optimize for? Current debates frame the risks of AI as a conflict between humans and machines. This brief argues instead that the central conflicts are between different groups of people, over the choice of the objectives that AI systems are built to maximize. Control over these objectives rests with those who control the inputs to AI, that is, the means of prediction: data, compute, expertise, and energy. To shed light on this control, I discuss the production function of AI, which maps data and compute into predictive performance, drawing on statistical learning theory and on the empirical scaling laws that have driven the industry’s costly scramble for scale and the resulting concentration of power. I then argue that market-based governance fails: individual property rights over data cannot address AI’s harms and benefits, because machine learning is fundamentally about data externalities, and because platform network effects are artificially maintained. I conclude with proposals for democratic control of the means of prediction, through institutions such as sortition and liquid democracy, to give those affected by algorithmic decisions a say over the objectives that AI pursues.